Edge-Based Smart Health Monitoring Device for Infectious Disease Prediction Using Biosensors

Due to the expanding growth of populations worldwide, infectious disease expenses are increasing rapidly. Patients with lesser chronic health problems are often encouraged to recover at home, which requires continuous remote monitoring and recommendation systems for decision-making. To overcome the above-mentioned challenges and reduce connectivity between infected patients and medical officers, we develop an artificial intelligence (AI)-based healthcare prototype in edge networks to monitor and analyze health parameters remotely. In the first phase of the work, health parameters are collected through multiple biosensors, including temperature, pulse rate, and oxygen saturation, and stored at the edge of the network. In the second phase, the monitoring data are preprocessed and analyzed using the neural network (NN) model in the edge devices for decision-making about the health status of the infected patient. Finally, the performance of the proposed model is evaluated using a real-time dataset, and multiple performance metrics are collected from the developed prototype to assess its effectiveness. The extensive simulation results demonstrate the efficiency of the proposed model over the existing ML models, i.e., the NN model achieves 94.05% accuracy, which is higher than the existing ones.